Detection of elusive polyps using a large-scale artificial intelligence system (with videos)
Dan M. Livovsky, Danny Veikherman, Tomer Golany, Amit Aides, Valentin Dashinsky, Nadav Rabani, David Ben Shimol, Yochai Blau, Liran Katzir, Ilan Shimshoni, Yun Liu, Ori Segol, Eran Goldin, Greg S. Corrado, Jesse Lachter, Yossi Matias, Ehud Rivlin, Daniel Z. Freedman
Abstract
BACKGROUND AND AIMS: on the detection of elusive polyps. METHODS: system was trained on 3611 hours of colonoscopy videos derived from 2 sources and was validated on a set comprising 1393 hours from a third unrelated source. Ground truth labeling was provided by offline gastroenterologist annotators who were able to watch the video in slow motion and pause and rewind as required. To assess applicability, stability, and user experience and to obtain some preliminary data on performance in a real-life scenario, a preliminary prospective clinical validation study was performed comprising 100 procedures. RESULTS: was able to detect polyps not seen by live real-time endoscopists or offline annotators in an average of .22 polyps per sequence. In the clinical validation study, the system detected an average of .89 additional polyps per procedure. No adverse events occurred. CONCLUSIONS: has a high sensitivity for polyp detection and was effective in increasing the detection of polyps both in colonoscopy videos and in real procedures with a low number of false alarms. (Clinical trial registration number: NCT04693078.).